import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A						=			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default='cifar10'     ,metadata={'help': 'Name of a dataset from the datasets package'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'The column name of the images in the files.'}			)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'A folder containing the training data.'}			)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'A folder containing the validation data.'}			)
		lowerCAmelCase_     =   field(
		    default=0.1_5     ,metadata={'help': 'Percent to split off of train for validation.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }     ,)
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Dict:
				_lowerCamelCase				   =  {}
				if self.train_dir is not None:
						_lowerCamelCase				   =  self.train_dir
				if self.validation_dir is not None:
						_lowerCamelCase				   =  self.validation_dir
				_lowerCamelCase				   =  data_files if data_files else None





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'Override some existing default config settings when a model is trained from scratch. Example: '
		            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}			)
		lowerCAmelCase_     =   field(
		    default='main'     ,metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Name or path of preprocessor config.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
		            'with private models).'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=0.7_5     ,metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Whether or not to train with normalized pixel values as target.'}			)





@dataclass
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default=1e-3     ,metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}			)



def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    int:
		_lowerCamelCase				   =  torch.stack([example['pixel_values'] for example in examples]							)
		return {"pixel_values": pixel_values}


def 	lowerCamelCase					(	)   ->    Dict:
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		_lowerCamelCase				   =  HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)							)
		if len(sys.argv							) == 2 and sys.argv[1].endswith('.json'							):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
		else:
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry('run_mae'			,       UpperCamelCase			,       UpperCamelCase							)

		# Setup logging
		logging.basicConfig(
		    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,       datefmt='%m/%d/%Y %H:%M:%S'			,       handlers=[logging.StreamHandler(sys.stdout							)]			,       )

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		_lowerCamelCase				   =  training_args.get_process_log_level()
		logger.setLevel(UpperCamelCase							)
		transformers.utils.logging.set_verbosity(UpperCamelCase							)
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
		    + F"""distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}"""							)
		logger.info(F"""Training/evaluation parameters {training_args}"""							)

		# Detecting last checkpoint.
		_lowerCamelCase				   =  None
		if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
				_lowerCamelCase				   =  get_last_checkpoint(training_args.output_dir							)
				if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
						raise ValueError(
						    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
						    'Use --overwrite_output_dir to overcome.'							)
				elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
						logger.info(
						    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
						    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'							)

    # Initialize our dataset.
		_lowerCamelCase				   =  load_dataset(
		    data_args.dataset_name			,       data_args.dataset_config_name			,       data_files=data_args.data_files			,       cache_dir=model_args.cache_dir			,       use_auth_token=True if model_args.use_auth_token else None			,       )

		# If we don't have a validation split, split off a percentage of train as validation.
		_lowerCamelCase				   =  None if 'validation' in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split			,       UpperCamelCase							) and data_args.train_val_split > 0.0:
				_lowerCamelCase				   =  ds['train'].train_test_split(data_args.train_val_split							)
				_lowerCamelCase				   =  split['train']
				_lowerCamelCase				   =  split['test']

		# Load pretrained model and image processor
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		_lowerCamelCase				   =  {
		    'cache_dir': model_args.cache_dir,
		    'revision': model_args.model_revision,
		    'use_auth_token': True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				_lowerCamelCase				   =  ViTMAEConfig.from_pretrained(model_args.config_name			,       **UpperCamelCase							)
		elif model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTMAEConfig.from_pretrained(model_args.model_name_or_path			,       **UpperCamelCase							)
		else:
				_lowerCamelCase				   =  ViTMAEConfig()
				logger.warning('You are instantiating a new config instance from scratch.'							)
				if model_args.config_overrides is not None:
						logger.info(F"""Overriding config: {model_args.config_overrides}"""							)
						config.update_from_string(model_args.config_overrides							)
						logger.info(F"""New config: {config}"""							)

    # adapt config
		config.update(
		    {
		        'mask_ratio': model_args.mask_ratio,
		        'norm_pix_loss': model_args.norm_pix_loss,
		    }							)

		# create image processor
		if model_args.image_processor_name:
				_lowerCamelCase				   =  ViTImageProcessor.from_pretrained(model_args.image_processor_name			,       **UpperCamelCase							)
		elif model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTImageProcessor.from_pretrained(model_args.model_name_or_path			,       **UpperCamelCase							)
		else:
				_lowerCamelCase				   =  ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTMAEForPreTraining.from_pretrained(
				    model_args.model_name_or_path			,       from_tf=bool('.ckpt' in model_args.model_name_or_path							)			,       config=UpperCamelCase			,       cache_dir=model_args.cache_dir			,       revision=model_args.model_revision			,       use_auth_token=True if model_args.use_auth_token else None			,       )
		else:
				logger.info('Training new model from scratch'							)
				_lowerCamelCase				   =  ViTMAEForPreTraining(UpperCamelCase							)

		if training_args.do_train:
				_lowerCamelCase				   =  ds['train'].column_names
		else:
				_lowerCamelCase				   =  ds['validation'].column_names

		if data_args.image_column_name is not None:
				_lowerCamelCase				   =  data_args.image_column_name
		elif "image" in column_names:
				_lowerCamelCase				   =  'image'
		elif "img" in column_names:
				_lowerCamelCase				   =  'img'
		else:
				_lowerCamelCase				   =  column_names[0]

		# transformations as done in original MAE paper
		# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
		if "shortest_edge" in image_processor.size:
				_lowerCamelCase				   =  image_processor.size['shortest_edge']
		else:
				_lowerCamelCase				   =  (image_processor.size['height'], image_processor.size['width'])
		_lowerCamelCase				   =  Compose(
		    [
		        Lambda(lambda UpperCamelCase							: img.convert('RGB'							) if img.mode != "RGB" else img							),
		        RandomResizedCrop(UpperCamelCase			,       scale=(0.2, 1.0)			,       interpolation=InterpolationMode.BICUBIC							),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean			,       std=image_processor.image_std							),
		    ]							)

		def preprocess_images(UpperCamelCase     :				Tuple							):
				_lowerCamelCase				   =  [transforms(UpperCamelCase							) for image in examples[image_column_name]]
				return examples

		if training_args.do_train:
				if "train" not in ds:
						raise ValueError('--do_train requires a train dataset'							)
				if data_args.max_train_samples is not None:
						_lowerCamelCase				   =  ds['train'].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
				# Set the training transforms
				ds["train"].set_transform(UpperCamelCase							)

		if training_args.do_eval:
				if "validation" not in ds:
						raise ValueError('--do_eval requires a validation dataset'							)
				if data_args.max_eval_samples is not None:
						_lowerCamelCase				   =  (
						    ds['validation'].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
						)
				# Set the validation transforms
				ds["validation"].set_transform(UpperCamelCase							)

		# Compute absolute learning rate
		_lowerCamelCase				   =  (
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				_lowerCamelCase				   =  training_args.base_learning_rate * total_train_batch_size / 2_56

		# Initialize our trainer
		_lowerCamelCase				   =  Trainer(
		    model=UpperCamelCase			,       args=UpperCamelCase			,       train_dataset=ds['train'] if training_args.do_train else None			,       eval_dataset=ds['validation'] if training_args.do_eval else None			,       tokenizer=UpperCamelCase			,       data_collator=UpperCamelCase			,       )

		# Training
		if training_args.do_train:
				_lowerCamelCase				   =  None
				if training_args.resume_from_checkpoint is not None:
						_lowerCamelCase				   =  training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						_lowerCamelCase				   =  last_checkpoint
				_lowerCamelCase				   =  trainer.train(resume_from_checkpoint=UpperCamelCase							)
				trainer.save_model()
				trainer.log_metrics('train'			,       train_result.metrics							)
				trainer.save_metrics('train'			,       train_result.metrics							)
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				_lowerCamelCase				   =  trainer.evaluate()
				trainer.log_metrics('eval'			,       UpperCamelCase							)
				trainer.save_metrics('eval'			,       UpperCamelCase							)

		# Write model card and (optionally) push to hub
		_lowerCamelCase				   =  {
		    'tasks': 'masked-auto-encoding',
		    'dataset': data_args.dataset_name,
		    'tags': ['masked-auto-encoding'],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**UpperCamelCase							)
		else:
				trainer.create_model_card(**UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Dict:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
				main()
import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
				    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
				    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
				    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
				    LayoutLMvaConfig,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				)
				from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import LayoutLMvaImageProcessor





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     int     ,  snake_case__     :     List[str]=2     ,  snake_case__     :     List[str]=3     ,  snake_case__     :     Tuple=4     ,  snake_case__     :     Optional[Any]=2     ,  snake_case__     :     int=7     ,  snake_case__     :     Optional[Any]=True     ,  snake_case__     :     Union[str, Any]=True     ,  snake_case__     :     Tuple=True     ,  snake_case__     :     int=True     ,  snake_case__     :     Optional[Any]=9_9     ,  snake_case__     :     Optional[Any]=3_6     ,  snake_case__     :     Tuple=3     ,  snake_case__     :     int=4     ,  snake_case__     :     Tuple=3_7     ,  snake_case__     :     Union[str, Any]="gelu"     ,  snake_case__     :     Any=0.1     ,  snake_case__     :     Optional[int]=0.1     ,  snake_case__     :     Dict=5_1_2     ,  snake_case__     :     Optional[int]=1_6     ,  snake_case__     :     Dict=2     ,  snake_case__     :     Dict=0.02     ,  snake_case__     :     List[Any]=6     ,  snake_case__     :     int=6     ,  snake_case__     :     Tuple=3     ,  snake_case__     :     List[Any]=4     ,  snake_case__     :     List[str]=None     ,  snake_case__     :     Optional[Any]=1_0_0_0     ,  )      ->	Optional[int]:
				_lowerCamelCase				   =  parent
				_lowerCamelCase				   =  batch_size
				_lowerCamelCase				   =  num_channels
				_lowerCamelCase				   =  image_size
				_lowerCamelCase				   =  patch_size
				_lowerCamelCase				   =  text_seq_length
				_lowerCamelCase				   =  is_training
				_lowerCamelCase				   =  use_input_mask
				_lowerCamelCase				   =  use_token_type_ids
				_lowerCamelCase				   =  use_labels
				_lowerCamelCase				   =  vocab_size
				_lowerCamelCase				   =  hidden_size
				_lowerCamelCase				   =  num_hidden_layers
				_lowerCamelCase				   =  num_attention_heads
				_lowerCamelCase				   =  intermediate_size
				_lowerCamelCase				   =  hidden_act
				_lowerCamelCase				   =  hidden_dropout_prob
				_lowerCamelCase				   =  attention_probs_dropout_prob
				_lowerCamelCase				   =  max_position_embeddings
				_lowerCamelCase				   =  type_vocab_size
				_lowerCamelCase				   =  type_sequence_label_size
				_lowerCamelCase				   =  initializer_range
				_lowerCamelCase				   =  coordinate_size
				_lowerCamelCase				   =  shape_size
				_lowerCamelCase				   =  num_labels
				_lowerCamelCase				   =  num_choices
				_lowerCamelCase				   =  scope
				_lowerCamelCase				   =  range_bbox

				# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
				_lowerCamelCase				   =  text_seq_length
				_lowerCamelCase				   =  (image_size // patch_size) ** 2 + 1
				_lowerCamelCase				   =  self.text_seq_length + self.image_seq_length
		def   _snake_case       (       self     :     int		)      ->	Any:
				_lowerCamelCase				   =  ids_tensor([self.batch_size, self.text_seq_length]     ,  self.vocab_size		)

				_lowerCamelCase				   =  ids_tensor([self.batch_size, self.text_seq_length, 4]     ,  self.range_bbox		)
				# Ensure that bbox is legal
				for i in range(bbox.shape[0]		):
						for j in range(bbox.shape[1]		):
								if bbox[i, j, 3] < bbox[i, j, 1]:
										_lowerCamelCase				   =  bbox[i, j, 3]
										_lowerCamelCase				   =  bbox[i, j, 1]
										_lowerCamelCase				   =  t
								if bbox[i, j, 2] < bbox[i, j, 0]:
										_lowerCamelCase				   =  bbox[i, j, 2]
										_lowerCamelCase				   =  bbox[i, j, 0]
										_lowerCamelCase				   =  t

				_lowerCamelCase				   =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				_lowerCamelCase				   =  None
				if self.use_input_mask:
						_lowerCamelCase				   =  random_attention_mask([self.batch_size, self.text_seq_length]		)

				_lowerCamelCase				   =  None
				if self.use_token_type_ids:
						_lowerCamelCase				   =  ids_tensor([self.batch_size, self.text_seq_length]     ,  self.type_vocab_size		)

				_lowerCamelCase				   =  None
				_lowerCamelCase				   =  None
				if self.use_labels:
						_lowerCamelCase				   =  ids_tensor([self.batch_size]     ,  self.type_sequence_label_size		)
						_lowerCamelCase				   =  ids_tensor([self.batch_size, self.text_seq_length]     ,  self.num_labels		)

				_lowerCamelCase				   =  LayoutLMvaConfig(
				    vocab_size=self.vocab_size     ,  hidden_size=self.hidden_size     ,  num_hidden_layers=self.num_hidden_layers     ,  num_attention_heads=self.num_attention_heads     ,  intermediate_size=self.intermediate_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  max_position_embeddings=self.max_position_embeddings     ,  type_vocab_size=self.type_vocab_size     ,  initializer_range=self.initializer_range     ,  coordinate_size=self.coordinate_size     ,  shape_size=self.shape_size     ,  input_size=self.image_size     ,  patch_size=self.patch_size     ,  )

				return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
		def   _snake_case       (       self     :     Optional[int]     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     Any     ,  snake_case__     :     str     ,  snake_case__     :     Tuple     ,  snake_case__     :     Dict     ,  snake_case__     :     Optional[int]     ,  snake_case__     :     int     ,  snake_case__     :     Optional[Any]		)      ->	Any:
				_lowerCamelCase				   =  LayoutLMvaModel(config=snake_case__		)
				model.to(snake_case__		)
				model.eval()

				# text + image
				_lowerCamelCase				   =  model(snake_case__     ,  pixel_values=snake_case__		)
				_lowerCamelCase				   =  model(
				    snake_case__     ,  bbox=snake_case__     ,  pixel_values=snake_case__     ,  attention_mask=snake_case__     ,  token_type_ids=snake_case__		)
				_lowerCamelCase				   =  model(snake_case__     ,  bbox=snake_case__     ,  pixel_values=snake_case__     ,  token_type_ids=snake_case__		)
				_lowerCamelCase				   =  model(snake_case__     ,  bbox=snake_case__     ,  pixel_values=snake_case__		)

				self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)		)

				# text only
				_lowerCamelCase				   =  model(snake_case__		)
				self.parent.assertEqual(
				    result.last_hidden_state.shape     ,  (self.batch_size, self.text_seq_length, self.hidden_size)		)

				# image only
				_lowerCamelCase				   =  model(pixel_values=snake_case__		)
				self.parent.assertEqual(
				    result.last_hidden_state.shape     ,  (self.batch_size, self.image_seq_length, self.hidden_size)		)
		def   _snake_case       (       self     :     Any     ,  snake_case__     :     Tuple     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     Optional[int]     ,  snake_case__     :     List[Any]     ,  snake_case__     :     Any     ,  snake_case__     :     str     ,  snake_case__     :     str     ,  snake_case__     :     int		)      ->	List[Any]:
				_lowerCamelCase				   =  self.num_labels
				_lowerCamelCase				   =  LayoutLMvaForSequenceClassification(snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(
				    snake_case__     ,  bbox=snake_case__     ,  pixel_values=snake_case__     ,  attention_mask=snake_case__     ,  token_type_ids=snake_case__     ,  labels=snake_case__     ,  )
				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)		)
		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     Tuple     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     str     ,  snake_case__     :     str     ,  snake_case__     :     Any     ,  snake_case__     :     List[str]     ,  snake_case__     :     Tuple     ,  snake_case__     :     Dict		)      ->	Optional[Any]:
				_lowerCamelCase				   =  self.num_labels
				_lowerCamelCase				   =  LayoutLMvaForTokenClassification(config=snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(
				    snake_case__     ,  bbox=snake_case__     ,  pixel_values=snake_case__     ,  attention_mask=snake_case__     ,  token_type_ids=snake_case__     ,  labels=snake_case__     ,  )
				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.text_seq_length, self.num_labels)		)
		def   _snake_case       (       self     :     str     ,  snake_case__     :     int     ,  snake_case__     :     Optional[int]     ,  snake_case__     :     str     ,  snake_case__     :     List[str]     ,  snake_case__     :     List[Any]     ,  snake_case__     :     List[Any]     ,  snake_case__     :     int     ,  snake_case__     :     Any		)      ->	Dict:
				_lowerCamelCase				   =  LayoutLMvaForQuestionAnswering(config=snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(
				    snake_case__     ,  bbox=snake_case__     ,  pixel_values=snake_case__     ,  attention_mask=snake_case__     ,  token_type_ids=snake_case__     ,  start_positions=snake_case__     ,  end_positions=snake_case__     ,  )
				self.parent.assertEqual(result.start_logits.shape     ,  (self.batch_size, self.seq_length)		)
				self.parent.assertEqual(result.end_logits.shape     ,  (self.batch_size, self.seq_length)		)



		def   _snake_case       (       self     :     str		)      ->	List[str]:
				_lowerCamelCase				   =  self.prepare_config_and_inputs()
				(
				    (
				    _lowerCamelCase
				)   ,							(
				    _lowerCamelCase
				)   ,							(
				    _lowerCamelCase
				)   ,							(
				    _lowerCamelCase
				)   ,							(
				    _lowerCamelCase
				)   ,							(
				    _lowerCamelCase
				)   ,							(
				    _lowerCamelCase
				)   ,							(
				    _lowerCamelCase
				)   ,							
				)				   =  config_and_inputs
				_lowerCamelCase				   =  {
				    'input_ids': input_ids,
				    'bbox': bbox,
				    'pixel_values': pixel_values,
				    'token_type_ids': token_type_ids,
				    'attention_mask': input_mask,
				}
				return config, inputs_dict





@require_torch
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False

		lowerCAmelCase_     =   (
		    (
		        LayoutLMvaModel,
		        LayoutLMvaForSequenceClassification,
		        LayoutLMvaForTokenClassification,
		        LayoutLMvaForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		lowerCAmelCase_     =   (
		    {'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
		    if is_torch_available()
		    else {}
		)
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Tuple     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     int     ,  snake_case__     :     Tuple     ,  snake_case__     :     Union[str, Any]		)      ->	str:
				# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
				# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
				# the sequence dimension of the text embedding only.
				# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
				return True
		def   _snake_case       (       self     :     str		)      ->	Any:
				_lowerCamelCase				   =  LayoutLMvaModelTester(self		)
				_lowerCamelCase				   =  ConfigTester(self     ,  config_class=snake_case__     ,  hidden_size=3_7		)
		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     str     ,  snake_case__     :     List[Any]     ,  snake_case__     :     Dict=False		)      ->	str:
				_lowerCamelCase				   =  copy.deepcopy(snake_case__		)
				if model_class in get_values(snake_case__		):
						_lowerCamelCase				   =  {
						    k: v.unsqueeze(1		).expand(-1     ,  self.model_tester.num_choices     ,  -1		).contiguous()
						    if isinstance(snake_case__     ,  torch.Tensor		) and v.ndim > 1
						    else v
						    for k, v in inputs_dict.items()
						}
				if return_labels:
						if model_class in get_values(snake_case__		):
								_lowerCamelCase				   =  torch.ones(self.model_tester.batch_size     ,  dtype=torch.long     ,  device=snake_case__		)
						elif model_class in get_values(snake_case__		):
								_lowerCamelCase				   =  torch.zeros(
								    self.model_tester.batch_size     ,  dtype=torch.long     ,  device=snake_case__		)
								_lowerCamelCase				   =  torch.zeros(
								    self.model_tester.batch_size     ,  dtype=torch.long     ,  device=snake_case__		)
						elif model_class in [
						    *get_values(snake_case__		),
						]:
								_lowerCamelCase				   =  torch.zeros(
								    self.model_tester.batch_size     ,  dtype=torch.long     ,  device=snake_case__		)
						elif model_class in [
						    *get_values(snake_case__		),
						]:
								_lowerCamelCase				   =  torch.zeros(
								    (self.model_tester.batch_size, self.model_tester.text_seq_length)     ,  dtype=torch.long     ,  device=snake_case__     ,  )

				return inputs_dict
		def   _snake_case       (       self     :     Any		)      ->	List[str]:
				self.config_tester.run_common_tests()
		def   _snake_case       (       self     :     int		)      ->	Optional[int]:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*snake_case__		)
		def   _snake_case       (       self     :     int		)      ->	Any:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
						_lowerCamelCase				   =  type
						self.model_tester.create_and_check_model(*snake_case__		)
		def   _snake_case       (       self     :     int		)      ->	Any:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*snake_case__		)
		def   _snake_case       (       self     :     Any		)      ->	Any:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*snake_case__		)
		def   _snake_case       (       self     :     int		)      ->	List[Any]:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*snake_case__		)



		@slow
		def   _snake_case       (       self     :     Optional[int]		)      ->	List[Any]:
				for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_lowerCamelCase				   =  LayoutLMvaModel.from_pretrained(snake_case__		)
						self.assertIsNotNone(snake_case__		)



def 	lowerCamelCase					(	)   ->    List[Any]:
		_lowerCamelCase				   =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
		return image





@require_torch
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		@cached_property
		def   _snake_case       (       self     :     int		)      ->	Any:
				return LayoutLMvaImageProcessor(apply_ocr=snake_case__		) if is_vision_available() else None



		@slow
		def   _snake_case       (       self     :     List[str]		)      ->	Tuple:
				_lowerCamelCase				   =  LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'		).to(snake_case__		)

				_lowerCamelCase				   =  self.default_image_processor
				_lowerCamelCase				   =  prepare_img()
				_lowerCamelCase				   =  image_processor(images=snake_case__     ,  return_tensors='pt'		).pixel_values.to(snake_case__		)

				_lowerCamelCase				   =  torch.tensor([[1, 2]]		)
				_lowerCamelCase				   =  torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]		).unsqueeze(0		)

				# forward pass
				_lowerCamelCase				   =  model(
				    input_ids=input_ids.to(snake_case__		)     ,  bbox=bbox.to(snake_case__		)     ,  pixel_values=pixel_values.to(snake_case__		)     ,  )

				# verify the logits
				_lowerCamelCase				   =  torch.Size((1, 1_9_9, 7_6_8)		)
				self.assertEqual(outputs.last_hidden_state.shape     ,  snake_case__		)

				_lowerCamelCase				   =  torch.tensor(
				    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]		).to(snake_case__		)

				self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,  snake_case__     ,  atol=1e-4		)		)